Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. Two types of methods for rule-based fuzzy modeling are studied. This classification concerns the form of the conclusion part of the rules that can be either constant or formed by some linear functions. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an auto-tuning algorithm that covers a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance tin-particular, the mapping and predicting capabilities of the rule-based computing). The study is illustrated with the aid of several representative numerical examples. (C) 2000 Elsevier Science B.V. All rights reserved.